On the use of mutations in Boolean minimization

The paper presents a new method of Boolean function minimization based on an original approach to implicant generation by inclusion of literals. The selection of these newly included literals, as well as the subsequent rejection of some others to obtain prime implicants, is based on heuristics working with the frequency of literal occurrence. Instead of using this data directly, some mutations are used in certain places in the algorithm. The technique of mutations and their influence on the quality of the result obtained is evaluated. The BOOM system implementing the proposed method is efficient especially for functions with several hundreds of input variables, whose values are defined only for a small part of their range. It has been tested both on standard benchmarks and on problems of a much larger dimension, generated randomly. These experiments proved that the new algorithm is very fast and that for large circuits it delivers better results than the state-of-the-art ESPRESSO.